code: def test_sel_with_default_parameters(df_test):
      X, y = df_test
      sel = SelectBySingleFeaturePerformance(
          RandomForestClassifier(random_state=1), threshold=0.5
      )
      sel.fit(X, y)
  
      # expected result
      Xtransformed = X.copy()
      Xtransformed.drop(columns=["var_3", "var_10"], inplace=True)
  
      # test init params
      assert sel.threshold == 0.5
      assert sel.cv == 3
      assert sel.scoring == "roc_auc"
      # test fit attrs
      assert sel.features_to_drop_ == ["var_3", "var_10"]
      assert sel.feature_performance_ == {
          "var_0": 0.5957642619540211,
          "var_1": 0.5365534287221033,
          "var_2": 0.5001855546283257,
          "var_3": 0.4752954458526748,
          "var_4": 0.9780875304971691,
          "var_5": 0.5065441419357082,
          "var_6": 0.9758243290622809,
          "var_7": 0.994571685008432,
          "var_8": 0.5164434795458892,
          "var_9": 0.9543427678969847,
          "var_10": 0.47404183834906727,
          "var_11": 0.5227164067525513,
      }
      # test transform output
      pd.testing.assert_frame_equal(sel.transform(X), Xtransformed)
  
file_path: feature_engine/tests/test_selection/test_single_feature_performance_selection.py
imports: import warnings
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.tree import DecisionTreeRegressor
  from feature_engine.selection import SelectBySingleFeaturePerformance
label_desc: Use image node_id 1 for calling a global function with example usage: test_sel_with_default_parameters(df_test) without return types